Core images, as a crucial digital image resource in the fields of geology, oil, and gas, are essential for scientific research and engineering practices. Their security is often ensured by adding digital watermarks. During digitization, core images frequently undergo JPEG compression when they are stored, transmitted, or published on Web pages. However, existing deep learning-based image digital watermarking algorithms still have significant shortcomings in terms of visual quality and robustness under JPEG compression. This study proposes an end-to-end image robust watermarking algorithm to address the issue of robust watermark embedding in core images under JPEG compression conditions. To efficiently integrate the features of the host image and the watermark, the study introduces a pyramid efficient multi-scale attention (PEMA) module. Through a unique cross-spatial interaction strategy and channel-wise relationship construction, the module effectively captures long-range dependencies in different directions and features information at various scales. To achieve visual imperceptibility, the study embeds the digital watermark into the low-frequency components of the host image using discrete wavelet transform (DWT) and introduces the DWT LL sub-band loss (DLL) loss function to improve the visual quality of the watermark image. Experimental results demonstrate that the proposed algorithm outperforms existing mainstream algorithms in both robustness against JPEG compression and visual imperceptibility.